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https://github.com/huggingface/lerobot.git
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Maximellerbach
54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
#!/usr/bin/env python
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from __future__ import annotations
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import pytest
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import torch
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from lerobot.policies.vla_jepa.world_model import (
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ActionConditionedVideoPredictor,
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)
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_ACTION_EMBED_DIM = 8
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def _make_predictor(
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embed_dim: int = 8,
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action_embed_dim: int = _ACTION_EMBED_DIM,
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predictor_embed_dim: int = 16,
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num_action_tokens: int = 2,
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) -> ActionConditionedVideoPredictor:
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return ActionConditionedVideoPredictor(
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embed_dim=embed_dim,
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action_embed_dim=action_embed_dim,
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predictor_embed_dim=predictor_embed_dim,
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depth=1,
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num_heads=2,
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mlp_ratio=2.0,
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num_action_tokens_per_step=num_action_tokens,
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)
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@pytest.mark.parametrize(
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"batch,num_steps,tokens_per_frame,embed_dim",
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[
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(1, 2, 1, 8),
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(2, 3, 4, 8),
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(4, 5, 2, 16),
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],
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)
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def test_predictor_output_shape(batch: int, num_steps: int, tokens_per_frame: int, embed_dim: int) -> None:
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predictor = _make_predictor(embed_dim=embed_dim, action_embed_dim=_ACTION_EMBED_DIM)
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frame_tokens = torch.randn(batch, num_steps, tokens_per_frame, embed_dim)
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action_tokens = torch.randn(batch, num_steps, 2, _ACTION_EMBED_DIM)
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out = predictor(frame_tokens, action_tokens)
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assert tuple(out.shape) == (batch, num_steps, tokens_per_frame, embed_dim)
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assert torch.isfinite(out).all()
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def test_predictor_step_mismatch_raises() -> None:
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predictor = _make_predictor()
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frame_tokens = torch.randn(2, 3, 4, 8)
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with pytest.raises(ValueError, match="Expected 3 action steps"):
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predictor(frame_tokens, torch.randn(2, 2, 2, 8))
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